plot.yardstick

Object of class <code>"yardstick"</code> to be plotted.
 This object is created by the command <code><a rd-options="" href="/link/yardstick?package=spatstat&version=1.64-1" data-mini-rdoc="spatstat::yardstick">yardstick</a></code>.

Additional graphics arguments passed to
 <code><a rd-options="graphics" href="/link/segments?package=spatstat&version=1.64-1&to=graphics" data-mini-rdoc="graphics::segments">segments</a></code> to control the appearance of the line.

&#8230;

Angle between the arrows and the line segment, in degrees.

angle

Length of arrow as a fraction of total length of the line segment.

frac

Logical. If <code>TRUE</code>, then the line will be broken in the
 middle, and the text will be placed in this gap. If <code>FALSE</code>,
 the line will be unbroken, and the text will be placed beside
 the line.

split

Fraction of total length to be removed from the middle of the
 line segment, if <code>split=TRUE</code>.

shrink

Integer (passed to <code><a rd-options="graphics" href="/link/text?package=spatstat&version=1.64-1&to=graphics" data-mini-rdoc="graphics::text">text</a></code>) determining the
 position of the annotation text relative to the line segment,
 if <code>split=FALSE</code>. Values of 1, 2, 3 and 4
 indicate positions below, to the left of,
 above and to the right of the line, respectively.

Optional list of additional arguments passed to
 <code><a rd-options="graphics" href="/link/text?package=spatstat&version=1.64-1&to=graphics" data-mini-rdoc="graphics::text">text</a></code> controlling the appearance of the text.
 Examples include <code>adj</code>, <code>srt</code>, <code>col</code>, <code>cex</code>,
 <code>font</code>.

txt.args

Optional numeric vector of length 2 specifying displacement
 of the text position relative to the centre of the yardstick.

txt.shift

Logical. Whether to actually perform the plot (<code>do.plot=TRUE</code>).

do.plot

Plots an object of class <code>"yardstick"</code>.

hplot

spatial

Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images.
Contains over 2000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference.
Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks.
Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods.
A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above.
Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.

Adrian Baddeley

spatstat

Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests

plot.yardstick function

Object of class <code>"yardstick"</code> to be plotted.
 This object is created by the command <code><a rd-options='' href='yardstick'>yardstick</a></code>.

Additional graphics arguments passed to
 <code><a rd-options='graphics' href='segments'>segments</a></code> to control the appearance of the line.

Integer (passed to <code><a rd-options='graphics' href='text'>text</a></code>) determining the
 position of the annotation text relative to the line segment,
 if <code>split=FALSE</code>. Values of 1, 2, 3 and 4
 indicate positions below, to the left of,
 above and to the right of the line, respectively.

Optional list of additional arguments passed to
 <code><a rd-options='graphics' href='text'>text</a></code> controlling the appearance of the text.
 Examples include <code>adj</code>, <code>srt</code>, <code>col</code>, <code>cex</code>,
 <code>font</code>.

plot.yardstick: Plot a Yardstick or Scale Bar

Description

Usage

Arguments

Value

Details

See Also

Examples